The present invention relates to voice activity detection and, more particularly, a low overhead voice activity detection technique for a noise-canceling bioacoustic sensor.
Noise-canceling bioacoustic sensors collect sounds from the human body that can be applied to a variety of medical and health diagnostic purposes, such as monitoring vital signs and detecting health problems. An exemplary sensor has a body microphone that collects body sounds of interest which is generally coupled to the body surface near the source of the sounds, such as at the chest or tracheal notch, and is oriented to have greatest sensitivity in the direction of the body. Despite best efforts to insulate the body microphone from the surrounding environment, environmental noise routinely leaks into the body sound channel. Accordingly, the sensor also has an environmental microphone that collects environmental sounds and is used to cancel environmental noise that infiltrates the body sound channel. In contrast to the body microphone, the environmental microphone is generally oriented to have greatest sensitivity in the direction away from the body.
In addition to dual microphones, noise-canceling bioacoustic sensors have active noise cancellation (ANC) systems. These ANC systems apply algorithms to the microphone signals to remove environmental noise from the body sound channel. These ANC systems then output a filtered body sound signal with environmental sounds greatly attenuated.
Speech can be a significant source of interference in bioacoustic sensing applications. In some applications, such as vital sign monitoring and health diagnostics, speech can corrupt the body sound signal, causing the application to perform poorly. In other bioacoustic sensing applications, it may be desirable to detect speech so that its informational content can be decoded and used.
Robust speech detection techniques that do not rely on ANC systems have been developed. For example, long-term spectral divergence voice activity detection techniques detect speech by processing a multiband spectral envelope over a rolling window. However, these techniques perform spectral analysis and statistical computations that are processor intensive and impose considerable system overhead.